Technical Field
The present disclosure generally relates to the field of machine learning. More specifically the present disclosure relates to the field of training of machine learning systems useful for evaluating entity pairs, as well as evaluating entity pairs using information generated by a trained machine learning system.
Description of the Related Art
Pairing or match-making finds use and purpose in many areas, from the smallest of interpersonal relationships to the largest of commercial partnerships. With the extraordinary reach of the Internet into virtually every country on the planet and the computational power of modern-day processors, the extension of personal and commercial pairing or match-making into the digital world could be viewed as inevitable.
In contrast to traditional digital models used in configuring device decision making, machine learning systems instead perform decision making based on connections or pathways established between processing elements. Such structure is more closely analogous to the interconnected neurological pathways found in a biological brain. Within a neural network type machine learning system, the organization and weights assigned to particular connections determine the ultimate heuristic value provided at the output layer of the neural network. Machine learning systems have been found to provide effective event predictions when trained using a large database of historical examples that promote the formation of connections within the machine learning system, the organization of the connections, and the weighting of the connections. During run-time operation of the machine learning system the organization and weighting of the connections provide the decision making capabilities within the machine learning system's system (e.g., hidden layer in neural networks). The run-time performance and accuracy of a machine learning system is to a large extent a function of these connections which, in turn, are dependent upon the quality, number, and types of prior examples provided during the training of the machine learning system.
After completing the training process, machine learning systems can derive meaning from complicated or imprecise data and can extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. In at least some instances, a trained machine learning system may be considered an “expert” in analyses performed on data that falls within the limits of training received by the machine learning system. As an “expert,” a trained machine learning system hastens the analysis and derivation of relationships within a large volume of data having numerous known and unknown dependencies. Such a data volume and the presence of known and unknown dependencies render comparable human analysis time-consuming at best, and near-impossible in many instances. The strength of machine learning systems lies in the analysis of voluminous and complex data with a high degree of accuracy to ascertain the answers to various “what if” type questions.
Computer networks, in particular the Internet, have opened a new arena where a first entity seeking a second entity, goods, or services are able to find such entities, goods or services matching one or more characteristics provided by the first entity. In a greatly simplified example, Joe who needs a hammer may visit an Internet marketplace, enter “hammer” and find that John makes and sells hammers. In another example, Joe may enter his interests, age, location, and other data on a Website to find potential friends or partners who are interested in meeting other people sharing similar interests or having similar life experiences. Such Websites often employ relatively simple matching criteria such as “Joe likes horses” and “Mary likes horses” to conclude that Joe and Mary would be compatible based on their apparent mutual interest in horses. The use of such a match-based selection or pairing process may however overlook instances where entities having one or more dissimilar, disparate, or different interests are found surprisingly compatible. Additionally, such match-based selection or pairing processes may fail to include near matches that may result in the formation of successful pairings or matches.
What is needed therefore are automated entity pairing systems and methods that are capable of matching or otherwise pairing entities having near, but non-matching, attributes or dissimilar attributes that may have been found compatible based on historically successful matches or pairings.